Python keras.applications.vgg16.preprocess_input() Examples
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Example #1
Source File: utils.py From neural-style-keras with MIT License | 9 votes |
def preprocess_image_scale(image_path, img_size=None): ''' Preprocess the image scaling it so that its larger size is max_size. This function preserves aspect ratio. ''' img = load_img(image_path) if img_size: scale = float(img_size) / max(img.size) new_size = (int(np.ceil(scale * img.size[0])), int(np.ceil(scale * img.size[1]))) img = img.resize(new_size, resample=Image.BILINEAR) img = img_to_array(img) img = np.expand_dims(img, axis=0) img = vgg16.preprocess_input(img) return img # util function to convert a tensor into a valid image
Example #2
Source File: test.py From Image-Caption-Generator with MIT License | 6 votes |
def extract_features(filename, model, model_type): if model_type == 'inceptionv3': from keras.applications.inception_v3 import preprocess_input target_size = (299, 299) elif model_type == 'vgg16': from keras.applications.vgg16 import preprocess_input target_size = (224, 224) # Loading and resizing image image = load_img(filename, target_size=target_size) # Convert the image pixels to a numpy array image = img_to_array(image) # Reshape data for the model image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2])) # Prepare the image for the CNN Model model image = preprocess_input(image) # Pass image into model to get encoded features features = model.predict(image, verbose=0) return features # Load the tokenizer
Example #3
Source File: extract_bottleneck_features.py From kale with Apache License 2.0 | 6 votes |
def extract_Xception(tensor): from keras.applications.xception import Xception, preprocess_input return Xception(weights='imagenet', include_top=False).predict(preprocess_input(tensor))
Example #4
Source File: vgg16_feature_extractor.py From keras-video-classifier with MIT License | 6 votes |
def extract_vgg16_features_live(model, video_input_file_path): print('Extracting frames from video: ', video_input_file_path) vidcap = cv2.VideoCapture(video_input_file_path) success, image = vidcap.read() features = [] success = True count = 0 while success: vidcap.set(cv2.CAP_PROP_POS_MSEC, (count * 1000)) # added this line success, image = vidcap.read() # print('Read a new frame: ', success) if success: img = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA) input = img_to_array(img) input = np.expand_dims(input, axis=0) input = preprocess_input(input) feature = model.predict(input).ravel() features.append(feature) count = count + 1 unscaled_features = np.array(features) return unscaled_features
Example #5
Source File: vgg16_feature_extractor.py From keras-video-classifier with MIT License | 6 votes |
def extract_vgg16_features(model, video_input_file_path, feature_output_file_path): if os.path.exists(feature_output_file_path): return np.load(feature_output_file_path) count = 0 print('Extracting frames from video: ', video_input_file_path) vidcap = cv2.VideoCapture(video_input_file_path) success, image = vidcap.read() features = [] success = True while success: vidcap.set(cv2.CAP_PROP_POS_MSEC, (count * 1000)) # added this line success, image = vidcap.read() # print('Read a new frame: ', success) if success: img = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA) input = img_to_array(img) input = np.expand_dims(input, axis=0) input = preprocess_input(input) feature = model.predict(input).ravel() features.append(feature) count = count + 1 unscaled_features = np.array(features) np.save(feature_output_file_path, unscaled_features) return unscaled_features
Example #6
Source File: utils.py From neural-style-keras with MIT License | 6 votes |
def preprocess_image_crop(image_path, img_size): ''' Preprocess the image scaling it so that its smaller size is img_size. The larger size is then cropped in order to produce a square image. ''' img = load_img(image_path) scale = float(img_size) / min(img.size) new_size = (int(np.ceil(scale * img.size[0])), int(np.ceil(scale * img.size[1]))) # print('old size: %s,new size: %s' %(str(img.size), str(new_size))) img = img.resize(new_size, resample=Image.BILINEAR) img = img_to_array(img) crop_h = img.shape[0] - img_size crop_v = img.shape[1] - img_size img = img[crop_h:img_size+crop_h, crop_v:img_size+crop_v, :] img = np.expand_dims(img, axis=0) img = vgg16.preprocess_input(img) return img # util function to open, resize and format pictures into appropriate tensors
Example #7
Source File: datasets.py From DEC-keras with MIT License | 6 votes |
def extract_vgg16_features(x): from keras.preprocessing.image import img_to_array, array_to_img from keras.applications.vgg16 import preprocess_input, VGG16 from keras.models import Model # im_h = x.shape[1] im_h = 224 model = VGG16(include_top=True, weights='imagenet', input_shape=(im_h, im_h, 3)) # if flatten: # add_layer = Flatten() # else: # add_layer = GlobalMaxPool2D() # feature_model = Model(model.input, add_layer(model.output)) feature_model = Model(model.input, model.get_layer('fc1').output) print('extracting features...') x = np.asarray([img_to_array(array_to_img(im, scale=False).resize((im_h,im_h))) for im in x]) x = preprocess_input(x) # data - 127. #data/255.# features = feature_model.predict(x) print('Features shape = ', features.shape) return features
Example #8
Source File: preprocessing.py From Image-Caption-Generator with MIT License | 6 votes |
def extract_features(path, model_type): if model_type == 'inceptionv3': from keras.applications.inception_v3 import preprocess_input target_size = (299, 299) elif model_type == 'vgg16': from keras.applications.vgg16 import preprocess_input target_size = (224, 224) # Get CNN Model from model.py model = CNNModel(model_type) features = dict() # Extract features from each photo for name in tqdm(os.listdir(path)): # Loading and resizing image filename = path + name image = load_img(filename, target_size=target_size) # Convert the image pixels to a numpy array image = img_to_array(image) # Reshape data for the model image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2])) # Prepare the image for the CNN Model model image = preprocess_input(image) # Pass image into model to get encoded features feature = model.predict(image, verbose=0) # Store encoded features for the image image_id = name.split('.')[0] features[image_id] = feature return features
Example #9
Source File: test_shap.py From AIX360 with Apache License 2.0 | 6 votes |
def test_ShapGradientExplainer(self): # model = VGG16(weights='imagenet', include_top=True) # X, y = shap.datasets.imagenet50() # to_explain = X[[39, 41]] # # url = "https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json" # fname = shap.datasets.cache(url) # with open(fname) as f: # class_names = json.load(f) # # def map2layer(x, layer): # feed_dict = dict(zip([model.layers[0].input], [preprocess_input(x.copy())])) # return K.get_session().run(model.layers[layer].input, feed_dict) # # e = GradientExplainer((model.layers[7].input, model.layers[-1].output), # map2layer(preprocess_input(X.copy()), 7)) # shap_values, indexes = e.explain_instance(map2layer(to_explain, 7), ranked_outputs=2) # print("Skipped Shap GradientExplainer")
Example #10
Source File: vgg.py From inpainting-gmcnn-keras with MIT License | 5 votes |
def _norm_inputs(input_img): ones = tf.constant(1, dtype=tf.float32) c = tf.constant(127.5, dtype=tf.float32) img_norm = Lambda(lambda x: x + ones)(input_img) img_norm = Lambda(lambda x: x * c)(img_norm) img_norm = Lambda(preprocess_input)(img_norm) return img_norm
Example #11
Source File: vggnet.py From bootcamp with Apache License 2.0 | 5 votes |
def vgg_extract_feat(img_path, model, graph, sess): with sess.as_default(): with graph.as_default(): img = image.load_img(img_path, target_size=(input_shape[0], input_shape[1])) img = image.img_to_array(img) img = np.expand_dims(img, axis=0) img = preprocess_input_vgg(img) feat = model.predict(img) norm_feat = feat[0] / LA.norm(feat[0]) norm_feat = [i.item() for i in norm_feat] return norm_feat
Example #12
Source File: deep_dream.py From costar_plan with Apache License 2.0 | 5 votes |
def preprocess_image(image_path): img = load_img(image_path, target_size=(img_width, img_height)) img = img_to_array(img) img = np.expand_dims(img, axis=0) img = vgg16.preprocess_input(img) return img # util function to convert a tensor into a valid image
Example #13
Source File: neural_style_transfer.py From pCVR with Apache License 2.0 | 5 votes |
def preprocess_image(image_path): img = load_img(image_path, target_size=(img_nrows, img_ncols)) img = img_to_array(img) img = np.expand_dims(img, axis=0) img = vgg16.preprocess_input(img) return img # util function to convert a tensor into a valid image
Example #14
Source File: utils.py From fast-neural-style-keras with MIT License | 5 votes |
def process_image(image_path, width, height, resize=True): if resize: img = load_img(image_path, target_size=(height, width)) else: img = load_img(image_path) img = img_to_array(img) img = np.expand_dims(img, axis=0) img = vgg16.preprocess_input(img) return img
Example #15
Source File: models.py From ICIAR2018 with MIT License | 5 votes |
def predict(self, x): if self.data_format == "channels_first": x = x.transpose(0, 3, 1, 2) x = preprocess_vgg(x.astype(K.floatx())) return self.model.predict(x, batch_size=self.batch_size)
Example #16
Source File: feature.py From Mosaicer with MIT License | 5 votes |
def preprocess_image(img): img_data = image.img_to_array(img) img_data = np.expand_dims(img_data, axis=0) img_data = preprocess_input(img_data) return img_data
Example #17
Source File: data_generator.py From AdvancedEAST with MIT License | 5 votes |
def gen(batch_size=cfg.batch_size, is_val=False): img_h, img_w = cfg.max_train_img_size, cfg.max_train_img_size x = np.zeros((batch_size, img_h, img_w, cfg.num_channels), dtype=np.float32) pixel_num_h = img_h // cfg.pixel_size pixel_num_w = img_w // cfg.pixel_size y = np.zeros((batch_size, pixel_num_h, pixel_num_w, 7), dtype=np.float32) if is_val: with open(os.path.join(cfg.data_dir, cfg.val_fname), 'r') as f_val: f_list = f_val.readlines() else: with open(os.path.join(cfg.data_dir, cfg.train_fname), 'r') as f_train: f_list = f_train.readlines() while True: for i in range(batch_size): # random gen an image name random_img = np.random.choice(f_list) img_filename = str(random_img).strip().split(',')[0] # load img and img anno img_path = os.path.join(cfg.data_dir, cfg.train_image_dir_name, img_filename) img = image.load_img(img_path) img = image.img_to_array(img) x[i] = preprocess_input(img, mode='tf') gt_file = os.path.join(cfg.data_dir, cfg.train_label_dir_name, img_filename[:-4] + '_gt.npy') y[i] = np.load(gt_file) yield x, y
Example #18
Source File: predict.py From AdvancedEAST with MIT License | 5 votes |
def predict_txt(east_detect, img_path, txt_path, pixel_threshold, quiet=False): img = image.load_img(img_path) d_wight, d_height = resize_image(img, cfg.max_predict_img_size) scale_ratio_w = d_wight / img.width scale_ratio_h = d_height / img.height img = img.resize((d_wight, d_height), Image.NEAREST).convert('RGB') img = image.img_to_array(img) img = preprocess_input(img, mode='tf') x = np.expand_dims(img, axis=0) y = east_detect.predict(x) y = np.squeeze(y, axis=0) y[:, :, :3] = sigmoid(y[:, :, :3]) cond = np.greater_equal(y[:, :, 0], pixel_threshold) activation_pixels = np.where(cond) quad_scores, quad_after_nms = nms(y, activation_pixels) txt_items = [] for score, geo in zip(quad_scores, quad_after_nms): if np.amin(score) > 0: rescaled_geo = geo / [scale_ratio_w, scale_ratio_h] rescaled_geo_list = np.reshape(rescaled_geo, (8,)).tolist() txt_item = ','.join(map(str, rescaled_geo_list)) txt_items.append(txt_item + '\n') elif not quiet: print('quad invalid with vertex num less then 4.') if cfg.predict_write2txt and len(txt_items) > 0: with open(txt_path, 'w') as f_txt: f_txt.writelines(txt_items)
Example #19
Source File: get_img_features_VGG16.py From machine-learning-note with MIT License | 5 votes |
def process_pic(img_path, model='', predict=True): img_path = img_path img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) # 下面两步不是很理解 x = np.expand_dims(x, axis=0) x = preprocess_input(x) if predict: # predict pic's class last_layer_features = model.predict(x) # 1000 last_layer_features # print('Predicted:', decode_predictions(last_layer_features, top=3)[0]) return decode_predictions(last_layer_features, top=3)[0] else: # return 4096 last_layer_features last_layer_features = model.predict(x) return last_layer_features
Example #20
Source File: vgg16_content_based_filtering.py From keras-recommender with MIT License | 5 votes |
def main(): data_dir_path = './data/ml-latest-small' poster_dir_path = './data/posters' output_dir_path = './data/models' np.set_printoptions(threshold=np.nan) pd.set_option('display.height', 1000) pd.set_option('display.max_rows', 500) pd.set_option('display.max_columns', 500) pd.set_option('display.width', 1000) df = pd.read_csv(data_dir_path + '/ratings.csv', sep=',') df_id = pd.read_csv(data_dir_path + '/links.csv', sep=',') df_movie_names = pd.read_csv(data_dir_path + '/movies.csv', sep=',') df = pd.merge(pd.merge(df, df_id, on='movieId'), df_movie_names, on='movieId') print(df.head()) data_file = data_dir_path + '/imdb_id_to_image_dict.data' if not os.path.exists(data_file): imdb_id_to_image_dict = dict() for poster_file in glob(poster_dir_path + '/*.jpg'): # debug here print('Loading img at {}'.format(poster_file)) img = kimage.load_img(poster_file, target_size=(224, 224)) img = preprocess_input(np.expand_dims(kimage.img_to_array(img), axis=0)) imdb_id = poster_file.split('/')[-1].split('.')[0] imdb_id_to_image_dict[imdb_id] = img pickle.dump(file=open(data_file, 'wb'), obj=imdb_id_to_image_dict) else: imdb_id_to_image_dict = pickle.load(file=open(data_file, 'rb')) recommender = Vgg16ContentBaseFiltering() recommender.fit(imdb_id_to_image_dict, model_dir_path=output_dir_path)
Example #21
Source File: tsne_grid.py From tsne-grid with MIT License | 5 votes |
def get_activations(model, img_collection): activations = [] for idx, img in enumerate(img_collection): if idx == to_plot: break; print("Processing image {}".format(idx+1)) img = img.resize((224, 224), Image.ANTIALIAS) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) activations.append(np.squeeze(model.predict(x))) return activations
Example #22
Source File: siamese.py From keras-face with MIT License | 5 votes |
def img_to_encoding(self, image_path): print('encoding: ', image_path) if self.vgg16_model is None: self.vgg16_model = self.create_vgg16_model() image = cv2.imread(image_path, 1) img = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA) input = img_to_array(img) input = np.expand_dims(input, axis=0) input = preprocess_input(input) return self.vgg16_model.predict(input)
Example #23
Source File: perceptual_model.py From style-image-prior with GNU General Public License v3.0 | 5 votes |
def call(self, inputs, mask=None): return self.__model(vgg16.preprocess_input(inputs))
Example #24
Source File: utils.py From deepxplore with MIT License | 5 votes |
def preprocess_image(img_path): img = image.load_img(img_path, target_size=(224, 224)) input_img_data = image.img_to_array(img) input_img_data = np.expand_dims(input_img_data, axis=0) input_img_data = preprocess_input(input_img_data) # final input shape = (1,224,224,3) return input_img_data
Example #25
Source File: utils.py From style-transfer with MIT License | 5 votes |
def preprocess_image(image_path, desired_dims): img = load_img(image_path, target_size=desired_dims) img = img_to_array(img) img = np.expand_dims(img, axis=0) img = vgg16.preprocess_input(img) return img # util function to convert a tensor into a valid image
Example #26
Source File: architectures.py From deepJDOT with MIT License | 5 votes |
def vgg16_fe(img_input): # net = preprocess_input(img_input) vgg_model = VGG16(weights='imagenet', include_top=True, input_tensor=img_input) vgg_model.layers.pop() return vgg_model.layers[-1].output # return model.layers[-1].output
Example #27
Source File: architectures.py From deepJDOT with MIT License | 5 votes |
def vgg16F_fe(img_input): # net = preprocess_input(img_input) from keras_vggface.vggface import VGGFace vgg_model = VGGFace(include_top=False, input_tensor=img_input, pooling='avg') #vgg_model.layers.pop() last_layer = vgg_model.get_layer('pool5').output x = Flatten(name='flatten')(last_layer) x = Dense(1024, activation='relu', trainable=True)(x) x = Dense(512, activation='relu', trainable=True)(x) model = dnn.Model(input=vgg_model.input, output=x) return model.layers[-1].output
Example #28
Source File: CNNFeatures.py From videofeatures with MIT License | 5 votes |
def computeFeatures(self, video): x = vgg16.preprocess_input(video) features = self.model.predict(x) return features
Example #29
Source File: CNNFeatures.py From videofeatures with MIT License | 5 votes |
def computeFeatures(self, video): x = resnet50.preprocess_input(video) features = self.model.predict(x) return features.reshape((-1, 2048))
Example #30
Source File: data.py From udacity-SDC-baseline with MIT License | 5 votes |
def vgg_preprocess_input(x): return vgg16.preprocess_input(x)